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Regression Analysis of Multivariate Fractional Data

Author

Listed:
  • José M.R. Murteira

    (Faculdade de Economia, Universidade de Coimbra, and CEMAPRE)

  • Joaquim J.S. Ramalho

    (Departamento de Economia and CEFAGE-UE, Universidade de Évora)

Abstract

The present article discusses alternative regression models and estimation methods for dealing with multivariate fractional response variables. Both conditional mean models, estimable by quasi-maximum likelihood, and fully parametric models (Dirichlet and Dirichletmultinomial), estimable by maximum likelihood, are considered. A new parameterization is proposed for the parametric models, which accommodates the most common specifications for the conditional mean (e.g., multinomial logit, nested logit, random parameters logit, dogit). The text also discusses at some length the specification analysis of fractional regression models, proposing several tests that can be performed through artificial regressions. Finally, an extensive Monte Carlo study evaluates the finite sample properties of most of the estimators and tests considered.

Suggested Citation

  • José M.R. Murteira & Joaquim J.S. Ramalho, 2013. "Regression Analysis of Multivariate Fractional Data," CEFAGE-UE Working Papers 2013_05, University of Evora, CEFAGE-UE (Portugal).
    • Handle: RePEc:cfe:wpcefa:2013_05
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    More about this item

    Keywords

    Multivariate fractional data; Quasi-maximum likelihood estimator; Dirichlet regression; Regression-based specification tests.;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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